20210601/0630_SS2_ZY, final
ILC2 IL25+/-, in vitro (02/)04/48/72h
(02h invitro ILC2 not stable, not involved in merged downstream analysis)
load processed data
TPM matrix
matt_raw <- read.table("./matrix/RNAseq.SS2_ZY_20210701.raw_tpm.gene.csv", header = T, sep = ",")
rownames(matt_raw) <- matt_raw$gene
matt_raw <- matt_raw[,2:ncol(matt_raw)]
matt_pc <- read.table("./matrix/RNAseq.SS2_ZY_20210701.filt_tpm.pc_gene.csv", header = T, sep = ",")
rownames(matt_pc) <- matt_pc$gene
matt_pc <- matt_pc[,2:ncol(matt_pc)]
idx.C04h <- c(13:16,9:12)
idx.C48h <- c(21:24,17:20)
idx.C72h <- c(29:32,25:28)
idx.IL25_C48hvsC04h <- c(21:24,13:16)
idx.IL25_C72hvsC04h <- c(29:32,13:16)
idx.IL25_C72hvsC48h <- c(29:32,21:24)
idx.nonIL25_C48hvsC04h <- c(17:20,9:12)
idx.nonIL25_C72hvsC04h <- c(25:28,9:12)
idx.nonIL25_C72hvsC48h <- c(25:28,17:20)dim(matt_raw)## [1] 55413 32head(matt_raw)## C02h1 C02h2 C02h3 C02h4 C02h5 C02h6 C02h7 C02h8 C04h1 C04h2 C04h3
## 0610005C13Rik 0.00 0.00 0.83 0.77 0.00 0.19 0.59 0.28 0.16 0.86 0.68
## 0610006L08Rik 0.00 0.51 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 0610009B22Rik 23.95 13.10 14.83 15.26 23.85 18.14 18.32 26.16 21.26 24.08 24.88
## 0610009E02Rik 3.32 0.50 0.00 5.02 0.65 0.00 2.90 0.00 0.97 0.00 0.37
## 0610009L18Rik 0.00 2.29 8.58 0.00 1.24 3.05 0.92 0.74 1.85 0.86 0.00
## 0610010F05Rik 0.62 2.52 2.67 2.36 2.69 3.58 4.11 2.97 3.59 4.49 5.33
## C04h4 C04h5 C04h6 C04h7 C04h8 C48h1 C48h2 C48h3 C48h4 C48h5 C48h6
## 0610005C13Rik 0.48 0.78 0.79 0.13 0.00 0.09 0.10 0.08 0.48 0.66 0.41
## 0610006L08Rik 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 0610009B22Rik 20.31 14.88 16.94 21.17 10.63 41.41 31.91 41.36 36.33 33.20 32.68
## 0610009E02Rik 0.69 0.67 0.00 0.00 0.00 0.00 1.14 0.00 2.06 0.00 0.68
## 0610009L18Rik 2.81 0.63 0.63 0.00 1.33 3.11 1.64 4.86 2.96 1.78 0.55
## 0610010F05Rik 6.08 5.10 5.59 3.20 4.13 3.08 3.56 4.39 5.36 5.30 6.01
## C48h7 C48h8 C72h1 C72h2 C72h3 C72h4 C72h5 C72h6 C72h7 C72h8
## 0610005C13Rik 0.27 0.76 0.00 0.16 0.00 0.43 0.36 0.00 0.00 0.22
## 0610006L08Rik 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 0610009B22Rik 37.04 32.29 33.17 44.37 39.30 35.51 25.00 31.87 32.94 29.36
## 0610009E02Rik 0.00 0.69 0.00 0.45 1.42 0.00 0.00 0.87 0.00 0.71
## 0610009L18Rik 1.37 1.51 0.51 1.28 1.80 2.01 1.92 0.00 3.11 3.27
## 0610010F05Rik 4.15 4.98 2.18 3.66 2.98 6.13 3.47 3.29 2.50 4.06dim(matt_pc)## [1] 10799 32head(matt_pc)## C02h1 C02h2 C02h3 C02h4 C02h5 C02h6 C02h7 C02h8 C04h1 C04h2 C04h3
## 0610009B22Rik 23.95 13.10 14.83 15.26 23.85 18.14 18.32 26.16 21.26 24.08 24.88
## 0610010F05Rik 0.62 2.52 2.67 2.36 2.69 3.58 4.11 2.97 3.59 4.49 5.33
## 0610010K14Rik 46.84 35.70 36.72 38.10 36.77 38.81 43.01 27.85 51.61 66.46 67.06
## 0610012G03Rik 44.66 40.21 31.76 26.73 29.16 28.98 27.51 38.09 45.28 40.28 33.81
## 0610030E20Rik 22.03 27.53 35.26 28.45 27.64 31.41 29.62 31.91 26.32 21.54 20.28
## 1110002E22Rik 0.00 0.30 0.10 0.00 0.55 0.41 0.54 0.32 0.22 0.23 0.25
## C04h4 C04h5 C04h6 C04h7 C04h8 C48h1 C48h2 C48h3 C48h4 C48h5
## 0610009B22Rik 20.31 14.88 16.94 21.17 10.63 41.41 31.91 41.36 36.33 33.20
## 0610010F05Rik 6.08 5.10 5.59 3.20 4.13 3.08 3.56 4.39 5.36 5.30
## 0610010K14Rik 61.19 58.70 68.08 66.62 55.93 104.24 113.16 90.76 93.41 75.29
## 0610012G03Rik 44.32 36.24 32.39 34.34 33.24 74.02 67.13 60.04 63.21 48.69
## 0610030E20Rik 22.55 25.56 19.27 19.53 22.83 15.62 20.53 20.98 21.39 10.88
## 1110002E22Rik 0.25 0.95 0.75 0.81 0.92 0.00 0.02 0.00 0.02 0.00
## C48h6 C48h7 C48h8 C72h1 C72h2 C72h3 C72h4 C72h5 C72h6 C72h7 C72h8
## 0610009B22Rik 32.68 37.04 32.29 33.17 44.37 39.30 35.51 25.00 31.87 32.94 29.36
## 0610010F05Rik 6.01 4.15 4.98 2.18 3.66 2.98 6.13 3.47 3.29 2.50 4.06
## 0610010K14Rik 83.29 86.47 76.81 76.53 90.27 90.21 80.46 47.72 74.76 66.51 63.84
## 0610012G03Rik 59.05 59.43 49.57 70.56 83.64 66.06 62.72 76.64 61.32 62.50 67.08
## 0610030E20Rik 10.61 11.35 10.58 18.00 23.29 19.15 24.96 11.92 12.06 12.79 9.80
## 1110002E22Rik 0.00 0.02 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00clustering
PCA
summary of PCs:
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 29.9929 21.2282 16.9714 10.24773 7.40695 6.56617 4.08178
## Proportion of Variance 0.4498 0.2253 0.1440 0.05251 0.02743 0.02156 0.00833
## Cumulative Proportion 0.4498 0.6751 0.8191 0.87163 0.89906 0.92062 0.92895
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 3.8207 3.6874 3.62840 3.36029 3.06920 2.87807 2.71365
## Proportion of Variance 0.0073 0.0068 0.00658 0.00565 0.00471 0.00414 0.00368
## Cumulative Proportion 0.9363 0.9430 0.94963 0.95527 0.95998 0.96412 0.96781
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Standard deviation 2.44441 2.35167 2.31266 2.20761 2.15059 2.05765 1.99155
## Proportion of Variance 0.00299 0.00277 0.00267 0.00244 0.00231 0.00212 0.00198
## Cumulative Proportion 0.97079 0.97356 0.97623 0.97867 0.98098 0.98310 0.98508
## PC22 PC23 PC24 PC25 PC26 PC27 PC28
## Standard deviation 1.98331 1.91795 1.82857 1.79572 1.73851 1.67774 1.63592
## Proportion of Variance 0.00197 0.00184 0.00167 0.00161 0.00151 0.00141 0.00134
## Cumulative Proportion 0.98705 0.98889 0.99056 0.99217 0.99368 0.99509 0.99643
## PC29 PC30 PC31 PC32
## Standard deviation 1.57397 1.5495 1.50452 1.7e-14
## Proportion of Variance 0.00124 0.0012 0.00113 0.0e+00
## Cumulative Proportion 0.99767 0.9989 1.00000 1.0e+00
tSNE
correlation of all filtered protein-coding genes, grouping by correlation
clustering (without C02h)
PCA
summary of PCs:
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 33.0232 21.6324 12.76393 8.80483 6.92260 4.39204 4.26366
## Proportion of Variance 0.5453 0.2340 0.08146 0.03876 0.02396 0.00964 0.00909
## Cumulative Proportion 0.5453 0.7792 0.86070 0.89947 0.92343 0.93307 0.94216
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 4.16336 3.86310 3.09133 2.94217 2.84744 2.6449 2.59506
## Proportion of Variance 0.00867 0.00746 0.00478 0.00433 0.00405 0.0035 0.00337
## Cumulative Proportion 0.95083 0.95829 0.96307 0.96740 0.97145 0.9749 0.97832
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Standard deviation 2.51401 2.37934 2.30841 2.24594 2.18831 2.07612 2.0488
## Proportion of Variance 0.00316 0.00283 0.00266 0.00252 0.00239 0.00216 0.0021
## Cumulative Proportion 0.98148 0.98431 0.98697 0.98949 0.99189 0.99404 0.9961
## PC22 PC23 PC24
## Standard deviation 2.00466 1.92337 1.08e-14
## Proportion of Variance 0.00201 0.00185 0.00e+00
## Cumulative Proportion 0.99815 1.00000 1.00e+00
tSNE
correlation of all filtered protein-coding genes, grouping by correlation
DEGs
mark_rowname <- function(DEGs){
rownames(DEGs) <- DEGs$gene
return(DEGs)
}
DEGs.C04h <- mark_rowname(read.table("./DEGs/IL25_vs_nonIL25/edgeR_DEGs_C04h5.6.7.8_vs_C04h1.2.3.4.csv",
header = T, sep = ","))
DEGs.C48h <- mark_rowname(read.table("./DEGs/IL25_vs_nonIL25/edgeR_DEGs_C48h5.6.7.8_vs_C48h1.2.3.4.csv",
header = T, sep = ","))
DEGs.C72h <- mark_rowname(read.table("./DEGs/IL25_vs_nonIL25/edgeR_DEGs_C72h5.6.7.8_vs_C72h1.2.3.4.csv",
header = T, sep = ","))
DEGs.IL25_C48hvsC04h <- mark_rowname(read.table("./DEGs/times_IL25/edgeR_DEGs_C48h5.6.7.8_vs_C04h5.6.7.8.csv",
header = T, sep = ","))
DEGs.IL25_C72hvsC04h <- mark_rowname(read.table("./DEGs/times_IL25/edgeR_DEGs_C72h5.6.7.8_vs_C04h5.6.7.8.csv",
header = T, sep = ","))
DEGs.IL25_C72hvsC48h <- mark_rowname(read.table("./DEGs/times_IL25/edgeR_DEGs_C72h5.6.7.8_vs_C48h5.6.7.8.csv",
header = T, sep = ","))
DEGs.nonIL25_C48hvsC04h <- mark_rowname(read.table("./DEGs/times_nonIL25/edgeR_DEGs_C48h1.2.3.4_vs_C04h1.2.3.4.csv",
header = T, sep = ","))
DEGs.nonIL25_C72hvsC04h <- mark_rowname(read.table("./DEGs/times_nonIL25/edgeR_DEGs_C72h1.2.3.4_vs_C04h1.2.3.4.csv",
header = T, sep = ","))
DEGs.nonIL25_C72hvsC48h <- mark_rowname(read.table("./DEGs/times_nonIL25/edgeR_DEGs_C72h1.2.3.4_vs_C48h1.2.3.4.csv",
header = T, sep = ","))C04h: IL25 vs nonIL25
using cutoff: p.adjust <0.05, FC >1.5
## C04h: IL25 vs nonIL25
##
## DEGs: 2139
## up: 807
## down: 1332
## Warning: The input is a data frame, convert it to the matrix.
## Warning: The input is a data frame, convert it to the matrix.
C48h: IL25 vs nonIL25
using cutoff: p.adjust <0.05, FC >1.5
## C48h: IL25 vs nonIL25
##
## DEGs: 3163
## up: 1530
## down: 1633
## Warning: The input is a data frame, convert it to the matrix.
## Warning: The input is a data frame, convert it to the matrix.
C72h: IL25 vs nonIL25
using cutoff: p.adjust <0.05, FC >1.5
## C72h: IL25 vs nonIL25
##
## DEGs: 2602
## up: 1269
## down: 1333
## Warning: The input is a data frame, convert it to the matrix.
## Warning: The input is a data frame, convert it to the matrix.
IL25: C48h vs C04h
using cutoff: p.adjust <0.01, FC >2
## IL25: C48h vs C04h
##
## DEGs: 3633
## up: 1925
## down: 1708
IL25: C72h vs C04h
using cutoff: p.adjust <0.01, FC >2
## IL25: C72h vs C04h
##
## DEGs: 4093
## up: 2218
## down: 1875
IL25: C72h vs C48h
using cutoff: p.adjust <0.05, FC >1.5
## IL25: C72h vs C48h
##
## DEGs: 1436
## up: 785
## down: 651
nonIL25: C48h vs C04h
using cutoff: p.adjust <0.01, FC >2
## nonIL25: C48h vs C04h
##
## DEGs: 2352
## up: 1310
## down: 1042
nonIL25: C72h vs C04h
using cutoff: p.adjust <0.01, FC >2
## nonIL25: C72h vs C04h
##
## DEGs: 2423
## up: 1305
## down: 1118
nonIL25: C72h vs C48h
using cutoff: p.adjust <0.05, FC >1.2
## nonIL25: C72h vs C48h
##
## DEGs: 60
## up: 33
## down: 27
